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observer-kb
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# PRD: Observer Knowledge Base
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**Status:** Active — extraction pipeline proven, needs multi-pass processing via LLM Team modes
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**Created:** 2026-03-28
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**Owner:** J
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**Branch:** `observer-kb`
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**Parent:** Lakehouse core (crates/)
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---
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## Problem
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AI conversations generate massive amounts of knowledge that's lost to context windows, hallucination, and scope drift. Current approach (single-pass extraction into categories) produces disorganized, hard-to-validate output. Reading raw pages is not useful — the system needs to process, clean, structure, and curate knowledge like an editorial team.
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## Vision
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A publishing pipeline that takes raw AI conversation exports and produces well-curated, validated knowledge documents. The output should be something you hand to a team — organized, filtered, and clearly presented. Questions anchor the content, with branching connections showing what was asked, what was answered, and how reasoning was directed.
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---
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## Architecture
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### Multi-Pass Processing via LLM Team Modes
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The key insight: devop.live already has 20 analysis modes that process content through structured workflows. The Observer KB should route uploaded data through these modes automatically.
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```
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Raw PDF Upload
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│
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▼ Mode: EXTRACT
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Structured facts, entities, relationships
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│
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▼ Mode: VALIDATOR
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Fact-check claims, score 1-10 confidence
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│
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▼ Mode: HALLUCINATION
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Verify each claim, flag likely hallucinations
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│
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▼ Mode: CONSENSUS
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Multiple passes iterate until extraction converges
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│
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▼ Mode: REDTEAM
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Find flaws in knowledge, patch them
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│
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▼ Mode: PIPELINE (custom)
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Clean → Q&A structure → topic grouping → value ranking
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│
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▼ RENDER
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Well-curated document: questions as anchors, branching connections,
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what was asked → answered → directed
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```
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### Integration with devop.live (LLM Team)
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The Observer KB calls the LLM Team API to run modes against extracted content:
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| Mode | Purpose in KB |
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|---|---|
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| **extract** | Structured facts, entities, relationships from raw text |
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| **validator** | Fact-check extracted claims, confidence scores |
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| **hallucination** | Verify each factual claim independently |
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| **drift** | Process N times, find consistent vs uncertain content |
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| **redteam** | Find flaws in extracted knowledge, improve |
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| **consensus** | Iterate until multiple passes agree on correct extraction |
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| **pipeline** | Chain: clean → extract → validate → organize → render |
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| **research** | Generate follow-up questions, research in parallel |
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| **blindassembly** | Split content into parts, process independently, reassemble |
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### Data Flow
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```
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Upload Portal (Observer KB :3400)
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│
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├── PDF → Lakehouse ingestd (text extraction, Parquet storage)
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│
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▼
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Processing Pipeline
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│
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├── Call devop.live API (:5000) with each mode
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├── Each mode processes content through local Ollama models
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├── Results stored back in Lakehouse (Parquet + catalog)
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│
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▼
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Knowledge Store
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│
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├── knowledge_facts: verified claims with confidence scores
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├── knowledge_qa: question → answer pairs with context
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├── knowledge_topics: auto-generated categories
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├── knowledge_connections: how topics relate across conversations
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├── knowledge_contradictions: flagged conflicts between conversations
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├── knowledge_validated: claims that passed hallucination check
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│
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▼
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Rendering
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│
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├── Structured knowledge view (web UI)
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├── Exportable curated document (PDF/markdown)
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└── Question-anchored navigation (Q → A → follow-up → related)
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```
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---
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## Processing Pipeline Detail
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### Pass 1: Clean + Structure
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- Strip PDF artifacts (headers, footers, page numbers)
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- Identify conversation turns (user/AI)
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- Fix encoding issues (ligatures, special chars)
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- Normalize formatting
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### Pass 2: Q&A Extraction
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- Identify questions asked by user
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- Map each question to its answer
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- Track follow-up questions and how they branch
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- Build question→answer→follow-up chains
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### Pass 3: Fact Extraction + Validation
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- Run EXTRACT mode: pull structured facts, entities, relationships
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- Run VALIDATOR mode: score each fact 1-10 confidence
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- Run HALLUCINATION mode: verify claims independently
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- Flag contradictions between conversations
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### Pass 4: Organize + Curate
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- Run CONSENSUS mode: multiple passes converge on correct categorization
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- Auto-generate topic categories from content (not predefined)
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- Rank content by value (decisions > insights > discussion > noise)
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- Connect related content across conversations
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### Pass 5: Render
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- Question-anchored view: each question is a node, answers branch from it
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- Topic-grouped view: knowledge organized by auto-generated categories
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- Timeline view: how thinking evolved across conversations
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- Export as clean markdown/PDF
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---
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## Current State (as of 2026-03-28)
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### What's Built
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- PDF ingestion (8 PDFs, 477 pages extracted)
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- Single-pass knowledge extraction (496 topics, 300 decisions, 100 insights)
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- Semantic search via HNSW (27ms over 477 chunks)
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- RAG (question → search → LLM answer with sources)
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- 5-pass deep analysis (corpus overview, patterns, evolution, gaps, synthesis)
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- Observer KB web UI (knowledge-first, not page-first)
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- Upload portal with drag & drop
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### What's Not Built Yet
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- Integration with devop.live LLM Team modes
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- Multi-pass processing pipeline (extract → validate → hallucination check)
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- Q&A chain extraction (question → answer → follow-up mapping)
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- Contradiction detection between conversations
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- Curated document export (PDF/markdown)
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- Question-anchored navigation
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- Auto-processing on upload (currently manual script)
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### What Needs to Happen Next Session
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1. **Integrate with devop.live API** — call modes programmatically
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2. **Build the multi-pass pipeline** — extract → validate → hallucinate-check → organize
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3. **Q&A chain extraction** — questions as anchors, answers branching
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4. **Auto-process on upload** — no manual script, pipeline runs automatically
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5. **Render curated output** — readable, hand-it-to-someone quality
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---
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## Tech Stack
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- **Storage:** Lakehouse storaged (Parquet on object storage)
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- **Catalog:** Lakehouse catalogd (metadata, PII, lineage)
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- **Query:** Lakehouse queryd (DataFusion SQL, hot cache, paged results)
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- **Vectors:** Lakehouse vectord (HNSW search, RAG)
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- **AI Processing:** devop.live LLM Team API (20 modes, multiple models)
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- **AI Runtime:** Ollama (local models — qwen2.5, mistral, gemma2, nomic-embed-text)
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- **UI:** Pure HTML/JS on :3400
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---
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## Non-Goals (for now)
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- Real-time conversation ingestion (PDF exports only)
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- Multi-user collaboration
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- Cloud deployment
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- Editing extracted knowledge from UI (read-only for now)
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@@ -0,0 +1,286 @@
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Observer KB</title>
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<style>
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:root{--bg:#0a0a0f;--s1:#12121a;--s2:#1a1a28;--b:#2a2a3a;--t:#e0e0e8;--d:#888898;--a:#6c5ce7;--ok:#00d2a0;--err:#ff6b6b;--warn:#ffa94d;--f:'JetBrains Mono','Fira Code',monospace}
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*{margin:0;padding:0;box-sizing:border-box}body{font-family:var(--f);background:var(--bg);color:var(--t);font-size:13px;min-height:100vh}
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||||
.app{max-width:1200px;margin:0 auto;padding:20px}
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||||
header{display:flex;align-items:center;gap:20px;padding:16px 0;border-bottom:1px solid var(--b);margin-bottom:20px}
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||||
header h1{font-size:20px;color:var(--a)}
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||||
.tabs{display:flex;gap:2px}.tab{background:none;border:none;color:var(--d);font-family:var(--f);font-size:12px;padding:10px 16px;cursor:pointer;border-bottom:2px solid transparent}
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.tab:hover{color:var(--t)}.tab.active{color:var(--a);border-bottom-color:var(--a)}
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.hdr-stats{margin-left:auto;font-size:11px;color:var(--d)}
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.btn{background:var(--a);color:white;border:none;padding:8px 16px;border-radius:6px;font-family:var(--f);font-size:12px;cursor:pointer;font-weight:600}
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.btn:hover{opacity:.9}.btn:disabled{opacity:.5}.btn-sm{padding:5px 10px;font-size:11px}
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.hidden{display:none}
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.stats-bar{display:grid;grid-template-columns:repeat(auto-fill,minmax(130px,1fr));gap:8px;margin-bottom:20px}
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.stat{background:var(--s1);border:1px solid var(--b);border-radius:8px;padding:12px;text-align:center}
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.stat-val{font-size:22px;font-weight:700;color:var(--a)}.stat-label{font-size:10px;color:var(--d);text-transform:uppercase;margin-top:2px}
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.section{margin-bottom:24px}.section h2{font-size:13px;color:var(--d);text-transform:uppercase;letter-spacing:.08em;margin-bottom:10px;border-bottom:1px solid var(--b);padding-bottom:6px}
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||||
.entry{background:var(--s1);border:1px solid var(--b);border-radius:8px;padding:14px;margin-bottom:8px}
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||||
.badge{font-size:9px;text-transform:uppercase;letter-spacing:.08em;padding:2px 8px;border-radius:3px;display:inline-block;margin-bottom:6px;font-weight:600}
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.badge-fact{background:var(--a);color:white}.badge-entity{background:var(--ok);color:white}
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.badge-decision{background:#e67e22;color:white}.badge-insight{background:#3498db;color:white}
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.badge-question{background:var(--warn);color:#000}.badge-verified{background:var(--ok);color:white}
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.entry-text{font-size:12px;line-height:1.6}.entry-src{font-size:10px;color:var(--d);margin-top:4px}
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.cat-grid{display:grid;grid-template-columns:repeat(auto-fill,minmax(250px,1fr));gap:8px;margin-bottom:16px}
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.cat-card{background:var(--s1);border:1px solid var(--b);border-radius:8px;padding:14px;cursor:pointer;border-left:3px solid var(--a);transition:all .15s}
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.cat-card:hover{border-color:var(--a)}.cat-title{font-weight:600;font-size:13px}.cat-count{font-size:10px;color:var(--d);margin-top:2px}
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.search-row{display:flex;gap:8px;margin-bottom:16px}
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.search-row input{flex:1;background:var(--s1);border:2px solid var(--b);color:var(--t);font-family:var(--f);font-size:14px;padding:12px;border-radius:8px;outline:none}
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.search-row input:focus{border-color:var(--a)}
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.ask-input{width:100%;background:var(--s1);border:2px solid var(--b);color:var(--t);font-family:var(--f);font-size:13px;padding:12px;border-radius:8px;resize:vertical;min-height:80px;outline:none;margin-bottom:8px}
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.ask-input:focus{border-color:var(--a)}
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.ai-answer{background:var(--s2);border:1px solid var(--b);border-radius:8px;padding:16px;margin-top:12px;line-height:1.7;white-space:pre-wrap}
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.ai-meta{font-size:10px;color:var(--d);margin-top:6px}
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.upload-zone{border:2px dashed var(--b);border-radius:12px;padding:36px;text-align:center;cursor:pointer;margin-bottom:16px}
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.upload-zone:hover{border-color:var(--a);background:rgba(108,92,231,.03)}
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.upload-zone h3{margin-bottom:4px}.upload-zone p{color:var(--d);font-size:11px}
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.file-item{display:flex;align-items:center;justify-content:space-between;padding:8px 12px;border-bottom:1px solid var(--b);font-size:12px}
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.done{color:var(--ok)}.failed{color:var(--err)}.uploading{color:var(--a)}
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.result{background:var(--s2);border:1px solid var(--b);border-radius:8px;padding:12px;margin-bottom:6px;border-left:3px solid var(--a)}
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.result-score{font-size:10px;color:var(--a);margin-bottom:2px}.result-text{font-size:12px;line-height:1.5}
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.empty{color:var(--d);text-align:center;padding:32px}
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</style>
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</head>
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<body>
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<div class="app">
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<header>
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<h1>OBSERVER KB</h1>
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<div class="tabs">
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<button class="tab active" onclick="showTab('knowledge')">Knowledge</button>
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<button class="tab" onclick="showTab('facts')">Facts & Entities</button>
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<button class="tab" onclick="showTab('explore')">Search</button>
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<button class="tab" onclick="showTab('ask')">Ask AI</button>
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<button class="tab" onclick="showTab('upload')">Ingest</button>
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</div>
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<div class="hdr-stats" id="hdr-stats"></div>
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</header>
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<div id="tab-knowledge"></div>
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<div id="tab-facts" class="hidden"></div>
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<div id="tab-explore" class="hidden"></div>
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<div id="tab-ask" class="hidden"></div>
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<div id="tab-upload" class="hidden"></div>
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</div>
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<script>
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const API=`http://${location.hostname}:3100`;
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const $=id=>document.getElementById(id);
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async function q(sql){return(await(await fetch(API+'/query/sql',{method:'POST',headers:{'Content-Type':'application/json'},body:JSON.stringify({sql})})).json())}
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async function searchHNSW(query,k=10){try{return(await(await fetch(API+'/vectors/hnsw/search',{method:'POST',headers:{'Content-Type':'application/json'},body:JSON.stringify({index_name:'observer_kb',query,top_k:k})})).json()).results||[]}catch{return[]}}
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function showTab(name){
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['knowledge','facts','explore','ask','upload'].forEach((t,i)=>{
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$('tab-'+t).classList.toggle('hidden',t!==name);
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document.querySelectorAll('.tab')[i].classList.toggle('active',t===name);
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});
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({knowledge:loadKnowledge,facts:loadFacts,explore:loadExplore,ask:loadAsk,upload:loadUpload})[name]();
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}
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function makeEntry(type,text,source){
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const e=document.createElement('div');e.className='entry';
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const b=document.createElement('span');b.className='badge badge-'+type;b.textContent=type;
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const t=document.createElement('div');t.className='entry-text';t.textContent=text;
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const s=document.createElement('div');s.className='entry-src';s.textContent=source;
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e.append(b,t,s);return e;
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}
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// === KNOWLEDGE (categories + decisions + insights) ===
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async function loadKnowledge(){
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const el=$('tab-knowledge');el.textContent='';
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try{
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const[topics,decisions,insights,questions,extractions]=await Promise.all([
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q("SELECT topic,COUNT(*) as n,COUNT(DISTINCT conversation) as c FROM knowledge_topics GROUP BY topic ORDER BY n DESC LIMIT 15"),
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q("SELECT conversation,page,decision FROM knowledge_decisions LIMIT 12"),
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q("SELECT conversation,page,insight FROM knowledge_insights LIMIT 8"),
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q("SELECT COUNT(*) as n FROM knowledge_questions"),
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q("SELECT type,COUNT(*) as n FROM processed_extractions GROUP BY type"),
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]);
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const factCount=extractions.rows.find(r=>r.type==='fact')?.n||0;
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const entityCount=extractions.rows.find(r=>r.type==='entity')?.n||0;
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||||
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// Stats
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const sb=document.createElement('div');sb.className='stats-bar';
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[{v:topics.row_count,l:'Categories'},{v:factCount,l:'Verified Facts'},{v:entityCount,l:'Entities'},{v:decisions.row_count,l:'Decisions'},{v:insights.row_count,l:'Insights'},{v:questions.rows[0]?.n||0,l:'Open Questions'}].forEach(s=>{
|
||||
const d=document.createElement('div');d.className='stat';
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||||
const v=document.createElement('div');v.className='stat-val';v.textContent=s.v;
|
||||
const l=document.createElement('div');l.className='stat-label';l.textContent=s.l;
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||||
d.append(v,l);sb.appendChild(d);
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});
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el.appendChild(sb);
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||||
$('hdr-stats').textContent=factCount+' facts · '+entityCount+' entities · '+decisions.row_count+' decisions';
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||||
|
||||
// Categories
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||||
const cs=document.createElement('div');cs.className='section';
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||||
cs.insertAdjacentHTML('beforeend','<h2>Auto-Generated Categories</h2>');
|
||||
const cg=document.createElement('div');cg.className='cat-grid';
|
||||
topics.rows.forEach(t=>{
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const c=document.createElement('div');c.className='cat-card';
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||||
const n=document.createElement('div');n.className='cat-title';n.textContent=t.topic;
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||||
const m=document.createElement('div');m.className='cat-count';m.textContent=t.n+' mentions · '+t.c+' conversations';
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||||
c.append(n,m);c.onclick=()=>drillTopic(t.topic);cg.appendChild(c);
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||||
});
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||||
cs.appendChild(cg);el.appendChild(cs);
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||||
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||||
// Decisions
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||||
const ds=document.createElement('div');ds.className='section';
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||||
ds.insertAdjacentHTML('beforeend','<h2>Key Decisions</h2>');
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||||
decisions.rows.forEach(d=>ds.appendChild(makeEntry('decision',d.decision,d.conversation+' p'+d.page)));
|
||||
el.appendChild(ds);
|
||||
|
||||
// Insights
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||||
const is=document.createElement('div');is.className='section';
|
||||
is.insertAdjacentHTML('beforeend','<h2>Key Insights</h2>');
|
||||
insights.rows.forEach(d=>is.appendChild(makeEntry('insight',d.insight,d.conversation+' p'+d.page)));
|
||||
el.appendChild(is);
|
||||
}catch(e){el.textContent='Error: '+e}
|
||||
}
|
||||
|
||||
async function drillTopic(topic){
|
||||
const el=$('tab-knowledge');el.textContent='';
|
||||
const back=document.createElement('button');back.className='btn btn-sm';back.textContent='← Back';back.onclick=loadKnowledge;
|
||||
const h=document.createElement('h2');h.style.cssText='font-size:18px;margin:12px 0';h.textContent=topic;
|
||||
el.append(back,h);
|
||||
const results=await searchHNSW(topic,10);
|
||||
const s=document.createElement('div');s.className='section';
|
||||
s.insertAdjacentHTML('beforeend','<h2>Related Content</h2>');
|
||||
results.forEach(r=>{
|
||||
const e=document.createElement('div');e.className='result';
|
||||
const sc=document.createElement('div');sc.className='result-score';sc.textContent='['+r.score.toFixed(3)+'] '+r.doc_id;
|
||||
const t=document.createElement('div');t.className='result-text';t.textContent=r.chunk_text.substring(0,300);
|
||||
e.append(sc,t);s.appendChild(e);
|
||||
});
|
||||
el.appendChild(s);
|
||||
}
|
||||
|
||||
// === FACTS & ENTITIES (from LLM Team extract mode) ===
|
||||
async function loadFacts(){
|
||||
const el=$('tab-facts');el.textContent='';
|
||||
try{
|
||||
const[facts,entities,verified]=await Promise.all([
|
||||
q("SELECT conversation,page,content FROM processed_extractions WHERE type='fact' ORDER BY conversation,page LIMIT 30"),
|
||||
q("SELECT conversation,content,COUNT(*) as n FROM processed_extractions WHERE type='entity' GROUP BY conversation,content ORDER BY n DESC LIMIT 30"),
|
||||
q("SELECT conversation,page,content FROM processed_validated LIMIT 10"),
|
||||
]);
|
||||
|
||||
const fs=document.createElement('div');fs.className='section';
|
||||
fs.insertAdjacentHTML('beforeend','<h2>Extracted Facts ('+facts.row_count+' total — via LLM Team Extract Mode)</h2>');
|
||||
facts.rows.forEach(r=>fs.appendChild(makeEntry('fact',r.content,r.conversation+' p'+r.page)));
|
||||
el.appendChild(fs);
|
||||
|
||||
const es=document.createElement('div');es.className='section';
|
||||
es.insertAdjacentHTML('beforeend','<h2>Entities ('+entities.row_count+' unique)</h2>');
|
||||
const eg=document.createElement('div');eg.style.cssText='display:flex;flex-wrap:wrap;gap:6px';
|
||||
entities.rows.forEach(r=>{
|
||||
const chip=document.createElement('span');
|
||||
chip.style.cssText='background:var(--s2);border:1px solid var(--b);padding:4px 10px;border-radius:4px;font-size:11px;cursor:pointer';
|
||||
chip.textContent=r.content+(r.n>1?' ('+r.n+')':'');
|
||||
chip.onclick=()=>{showTab('explore');setTimeout(()=>{document.getElementById('searchInput').value=r.content;doSearch()},100)};
|
||||
eg.appendChild(chip);
|
||||
});
|
||||
es.appendChild(eg);el.appendChild(es);
|
||||
|
||||
if(verified.rows.length){
|
||||
const vs=document.createElement('div');vs.className='section';
|
||||
vs.insertAdjacentHTML('beforeend','<h2>Verified by Secondary Model</h2>');
|
||||
verified.rows.forEach(r=>vs.appendChild(makeEntry('verified',r.content,r.conversation+' p'+r.page)));
|
||||
el.appendChild(vs);
|
||||
}
|
||||
}catch(e){el.textContent='Error: '+e}
|
||||
}
|
||||
|
||||
// === SEARCH ===
|
||||
function loadExplore(){
|
||||
const el=$('tab-explore');el.textContent='';
|
||||
const row=document.createElement('div');row.className='search-row';
|
||||
const input=document.createElement('input');input.id='searchInput';input.placeholder='Search by meaning across all knowledge...';
|
||||
const btn=document.createElement('button');btn.className='btn';btn.textContent='Search';
|
||||
btn.onclick=doSearch;input.onkeydown=e=>{if(e.key==='Enter')doSearch()};
|
||||
row.append(input,btn);
|
||||
const res=document.createElement('div');res.id='searchResults';
|
||||
const hint=document.createElement('div');hint.className='empty';hint.textContent='Try: "object storage architecture", "data ingestion trade-offs", "CRM options"';
|
||||
el.append(row,res,hint);
|
||||
}
|
||||
async function doSearch(){
|
||||
const query=document.getElementById('searchInput').value;if(!query)return;
|
||||
const el=$('searchResults');el.textContent='searching...';
|
||||
const results=await searchHNSW(query,15);
|
||||
el.textContent='';
|
||||
if(!results.length){el.textContent='No results';return}
|
||||
results.forEach(r=>{
|
||||
const e=document.createElement('div');e.className='result';
|
||||
const s=document.createElement('div');s.className='result-score';s.textContent='['+r.score.toFixed(3)+'] '+r.doc_id.replace(/_/g,' ');
|
||||
const t=document.createElement('div');t.className='result-text';t.textContent=r.chunk_text.substring(0,400);
|
||||
e.append(s,t);el.appendChild(e);
|
||||
});
|
||||
}
|
||||
|
||||
// === ASK AI ===
|
||||
function loadAsk(){
|
||||
const el=$('tab-ask');el.textContent='';
|
||||
const ta=document.createElement('textarea');ta.className='ask-input';ta.id='askInput';
|
||||
ta.placeholder='Ask a question about your knowledge base...\n\nExamples:\n- What architecture decisions were made about Iceberg?\n- Compare Spark vs Flink approaches\n- What approaches were abandoned and why?';
|
||||
const btn=document.createElement('button');btn.className='btn';btn.id='askBtn';btn.textContent='Ask AI';
|
||||
btn.onclick=doAsk;
|
||||
const resp=document.createElement('div');resp.id='askResp';
|
||||
el.append(ta,btn,resp);
|
||||
}
|
||||
async function doAsk(){
|
||||
const question=$('askInput').value;if(!question)return;
|
||||
$('askBtn').disabled=true;$('askBtn').textContent='thinking...';
|
||||
const el=$('askResp');el.textContent='searching knowledge...';
|
||||
try{
|
||||
const r=await(await fetch(API+'/vectors/rag',{method:'POST',headers:{'Content-Type':'application/json'},body:JSON.stringify({index_name:'observer_kb',question,top_k:5})})).json();
|
||||
el.textContent='';
|
||||
const ans=document.createElement('div');ans.className='ai-answer';ans.textContent=r.answer;
|
||||
const meta=document.createElement('div');meta.className='ai-meta';meta.textContent='Model: '+r.model+(r.tokens_generated?' · '+r.tokens_generated+' tokens':'');
|
||||
el.append(ans,meta);
|
||||
if(r.sources?.length){
|
||||
const sh=document.createElement('h2');sh.style.cssText='font-size:11px;color:var(--d);margin-top:14px';sh.textContent='SOURCES';el.appendChild(sh);
|
||||
r.sources.forEach(s=>{
|
||||
const e=document.createElement('div');e.className='result';e.style.marginTop='4px';
|
||||
const sc=document.createElement('div');sc.className='result-score';sc.textContent='['+s.score.toFixed(3)+'] '+s.doc_id;
|
||||
const t=document.createElement('div');t.className='result-text';t.style.fontSize='11px';t.textContent=s.chunk_text.substring(0,200);
|
||||
e.append(sc,t);el.appendChild(e);
|
||||
});
|
||||
}
|
||||
}catch(e){el.textContent='Error: '+e}
|
||||
$('askBtn').disabled=false;$('askBtn').textContent='Ask AI';
|
||||
}
|
||||
|
||||
// === UPLOAD ===
|
||||
function loadUpload(){
|
||||
const el=$('tab-upload');el.textContent='';
|
||||
const zone=document.createElement('div');zone.className='upload-zone';
|
||||
zone.onclick=()=>{const i=document.createElement('input');i.type='file';i.multiple=true;i.accept='.pdf,.txt,.csv,.json';i.onchange=e=>[...e.target.files].forEach(uploadFile);i.click()};
|
||||
zone.ondragover=e=>{e.preventDefault();zone.style.borderColor='var(--a)'};
|
||||
zone.ondragleave=()=>zone.style.borderColor='';
|
||||
zone.ondrop=e=>{e.preventDefault();zone.style.borderColor='';[...e.dataTransfer.files].forEach(uploadFile)};
|
||||
const h=document.createElement('h3');h.textContent='Drop files to ingest';
|
||||
const p=document.createElement('p');p.textContent='PDF, CSV, JSON, Text — auto-extracted and indexed';
|
||||
zone.append(h,p);
|
||||
const list=document.createElement('div');list.id='upList';
|
||||
el.append(zone,list);
|
||||
}
|
||||
function uploadFile(file){
|
||||
const item=document.createElement('div');item.className='file-item';
|
||||
const name=document.createElement('span');name.textContent=file.name;
|
||||
const status=document.createElement('span');status.className='uploading';status.textContent='uploading...';
|
||||
item.append(name,status);$('upList').prepend(item);
|
||||
const fd=new FormData();fd.append('file',file);
|
||||
const xhr=new XMLHttpRequest();xhr.open('POST',API+'/ingest/file');
|
||||
xhr.upload.onprogress=e=>{if(e.lengthComputable)status.textContent=Math.round(e.loaded/e.total*100)+'%'};
|
||||
xhr.onload=()=>{status.className=xhr.status<300?'done':'failed';status.textContent=xhr.status<300?'ingested':'failed'};
|
||||
xhr.onerror=()=>{status.className='failed';status.textContent='error'};
|
||||
xhr.send(fd);
|
||||
}
|
||||
|
||||
loadKnowledge();
|
||||
</script>
|
||||
</body>
|
||||
</html>
|
||||
@@ -0,0 +1,235 @@
|
||||
/// ChatGPT PDF conversation parser.
|
||||
/// Extracts structured conversation turns from ChatGPT export PDFs.
|
||||
/// Handles: user/assistant turns, timestamps, conversation titles.
|
||||
|
||||
use arrow::array::{ArrayRef, Int32Array, RecordBatch, StringArray};
|
||||
use arrow::datatypes::{DataType, Field, Schema};
|
||||
use std::sync::Arc;
|
||||
|
||||
/// A single conversation turn.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct ConversationTurn {
|
||||
pub source_file: String,
|
||||
pub conversation_title: String,
|
||||
pub turn_number: i32,
|
||||
pub role: String, // "user" or "assistant"
|
||||
pub content: String,
|
||||
pub page_number: i32,
|
||||
}
|
||||
|
||||
/// Parse a ChatGPT PDF into structured conversation turns.
|
||||
pub fn parse_chatgpt_pdf(content: &[u8], filename: &str) -> Result<(Arc<Schema>, Vec<RecordBatch>), String> {
|
||||
let doc = lopdf::Document::load_mem(content)
|
||||
.map_err(|e| format!("PDF load error: {e}"))?;
|
||||
|
||||
// Extract all text
|
||||
let pages = doc.get_pages();
|
||||
let mut all_text = String::new();
|
||||
let mut page_texts: Vec<(i32, String)> = Vec::new();
|
||||
|
||||
for (&page_num, _) in pages.iter() {
|
||||
let text = doc.extract_text(&[page_num]).unwrap_or_default();
|
||||
let text = text.trim().to_string();
|
||||
if !text.is_empty() {
|
||||
page_texts.push((page_num as i32, text.clone()));
|
||||
all_text.push_str(&text);
|
||||
all_text.push('\n');
|
||||
}
|
||||
}
|
||||
|
||||
if all_text.trim().is_empty() {
|
||||
return Err("PDF contains no extractable text".into());
|
||||
}
|
||||
|
||||
// Try to parse as ChatGPT conversation format
|
||||
let turns = extract_turns(&all_text, filename, &page_texts);
|
||||
|
||||
if turns.is_empty() {
|
||||
// Fall back to page-based extraction if we can't detect conversation structure
|
||||
return parse_as_raw_pages(&page_texts, filename);
|
||||
}
|
||||
|
||||
tracing::info!("parsed ChatGPT PDF '{}': {} turns", filename, turns.len());
|
||||
turns_to_batch(&turns)
|
||||
}
|
||||
|
||||
/// Extract conversation turns from ChatGPT PDF text.
|
||||
/// ChatGPT PDFs typically have patterns like:
|
||||
/// - "You said:" / "ChatGPT said:" (newer format)
|
||||
/// - "User:" / "Assistant:"
|
||||
/// - Or just alternating blocks separated by formatting
|
||||
fn extract_turns(text: &str, filename: &str, page_texts: &[(i32, String)]) -> Vec<ConversationTurn> {
|
||||
let mut turns = Vec::new();
|
||||
let mut turn_num = 0;
|
||||
|
||||
// Try to extract conversation title from first line
|
||||
let title = text.lines().next()
|
||||
.map(|l| l.trim().to_string())
|
||||
.filter(|l| l.len() < 200 && !l.is_empty())
|
||||
.unwrap_or_else(|| filename.replace(".pdf", ""));
|
||||
|
||||
// Strategy 1: Look for "You said:" / "ChatGPT said:" markers (common format)
|
||||
let you_markers = ["You said:", "You:", "User:", "Human:"];
|
||||
let ai_markers = ["ChatGPT said:", "ChatGPT:", "Assistant:", "AI:"];
|
||||
|
||||
let has_markers = you_markers.iter().any(|m| text.contains(m))
|
||||
&& ai_markers.iter().any(|m| text.contains(m));
|
||||
|
||||
if has_markers {
|
||||
let lines: Vec<&str> = text.lines().collect();
|
||||
let mut current_role = String::new();
|
||||
let mut current_content = String::new();
|
||||
let mut current_page = 1i32;
|
||||
|
||||
for line in &lines {
|
||||
let trimmed = line.trim();
|
||||
|
||||
// Check for role markers
|
||||
let mut found_marker = false;
|
||||
for marker in &you_markers {
|
||||
if trimmed.starts_with(marker) || trimmed == *marker {
|
||||
// Save previous turn
|
||||
if !current_content.trim().is_empty() && !current_role.is_empty() {
|
||||
turn_num += 1;
|
||||
turns.push(ConversationTurn {
|
||||
source_file: filename.to_string(),
|
||||
conversation_title: title.clone(),
|
||||
turn_number: turn_num,
|
||||
role: current_role.clone(),
|
||||
content: current_content.trim().to_string(),
|
||||
page_number: current_page,
|
||||
});
|
||||
}
|
||||
current_role = "user".to_string();
|
||||
current_content = trimmed.strip_prefix(marker).unwrap_or("").trim().to_string();
|
||||
current_content.push('\n');
|
||||
found_marker = true;
|
||||
break;
|
||||
}
|
||||
}
|
||||
if !found_marker {
|
||||
for marker in &ai_markers {
|
||||
if trimmed.starts_with(marker) || trimmed == *marker {
|
||||
if !current_content.trim().is_empty() && !current_role.is_empty() {
|
||||
turn_num += 1;
|
||||
turns.push(ConversationTurn {
|
||||
source_file: filename.to_string(),
|
||||
conversation_title: title.clone(),
|
||||
turn_number: turn_num,
|
||||
role: current_role.clone(),
|
||||
content: current_content.trim().to_string(),
|
||||
page_number: current_page,
|
||||
});
|
||||
}
|
||||
current_role = "assistant".to_string();
|
||||
current_content = trimmed.strip_prefix(marker).unwrap_or("").trim().to_string();
|
||||
current_content.push('\n');
|
||||
found_marker = true;
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if !found_marker && !current_role.is_empty() {
|
||||
current_content.push_str(trimmed);
|
||||
current_content.push('\n');
|
||||
}
|
||||
}
|
||||
|
||||
// Save last turn
|
||||
if !current_content.trim().is_empty() && !current_role.is_empty() {
|
||||
turn_num += 1;
|
||||
turns.push(ConversationTurn {
|
||||
source_file: filename.to_string(),
|
||||
conversation_title: title.clone(),
|
||||
turn_number: turn_num,
|
||||
role: current_role,
|
||||
content: current_content.trim().to_string(),
|
||||
page_number: current_page,
|
||||
});
|
||||
}
|
||||
|
||||
return turns;
|
||||
}
|
||||
|
||||
// Strategy 2: Split by paragraph blocks, alternate user/assistant
|
||||
// This is a heuristic fallback for PDFs without clear markers
|
||||
let paragraphs: Vec<&str> = text.split("\n\n")
|
||||
.map(|p| p.trim())
|
||||
.filter(|p| p.len() > 20) // skip short fragments
|
||||
.collect();
|
||||
|
||||
if paragraphs.len() >= 2 {
|
||||
for (i, para) in paragraphs.iter().enumerate() {
|
||||
turn_num += 1;
|
||||
turns.push(ConversationTurn {
|
||||
source_file: filename.to_string(),
|
||||
conversation_title: title.clone(),
|
||||
turn_number: turn_num,
|
||||
role: if i % 2 == 0 { "user" } else { "assistant" }.to_string(),
|
||||
content: para.to_string(),
|
||||
page_number: 1,
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
turns
|
||||
}
|
||||
|
||||
/// Fallback: parse as raw pages with full text.
|
||||
fn parse_as_raw_pages(page_texts: &[(i32, String)], filename: &str) -> Result<(Arc<Schema>, Vec<RecordBatch>), String> {
|
||||
let schema = Arc::new(Schema::new(vec![
|
||||
Field::new("source_file", DataType::Utf8, false),
|
||||
Field::new("conversation_title", DataType::Utf8, false),
|
||||
Field::new("turn_number", DataType::Int32, false),
|
||||
Field::new("role", DataType::Utf8, false),
|
||||
Field::new("content", DataType::Utf8, false),
|
||||
Field::new("page_number", DataType::Int32, false),
|
||||
]));
|
||||
|
||||
let sources: Vec<&str> = page_texts.iter().map(|_| filename).collect();
|
||||
let titles: Vec<String> = page_texts.iter().map(|_| filename.replace(".pdf", "")).collect();
|
||||
let turn_nums: Vec<i32> = (1..=page_texts.len() as i32).collect();
|
||||
let roles: Vec<&str> = page_texts.iter().map(|_| "page").collect();
|
||||
let contents: Vec<&str> = page_texts.iter().map(|(_, t)| t.as_str()).collect();
|
||||
let pages: Vec<i32> = page_texts.iter().map(|(p, _)| *p).collect();
|
||||
|
||||
let arrays: Vec<ArrayRef> = vec![
|
||||
Arc::new(StringArray::from(sources)),
|
||||
Arc::new(StringArray::from(titles.iter().map(|s| s.as_str()).collect::<Vec<_>>())),
|
||||
Arc::new(Int32Array::from(turn_nums)),
|
||||
Arc::new(StringArray::from(roles)),
|
||||
Arc::new(StringArray::from(contents)),
|
||||
Arc::new(Int32Array::from(pages)),
|
||||
];
|
||||
|
||||
let batch = RecordBatch::try_new(schema.clone(), arrays)
|
||||
.map_err(|e| format!("RecordBatch error: {e}"))?;
|
||||
|
||||
Ok((schema, vec![batch]))
|
||||
}
|
||||
|
||||
fn turns_to_batch(turns: &[ConversationTurn]) -> Result<(Arc<Schema>, Vec<RecordBatch>), String> {
|
||||
let schema = Arc::new(Schema::new(vec![
|
||||
Field::new("source_file", DataType::Utf8, false),
|
||||
Field::new("conversation_title", DataType::Utf8, false),
|
||||
Field::new("turn_number", DataType::Int32, false),
|
||||
Field::new("role", DataType::Utf8, false),
|
||||
Field::new("content", DataType::Utf8, false),
|
||||
Field::new("page_number", DataType::Int32, false),
|
||||
]));
|
||||
|
||||
let arrays: Vec<ArrayRef> = vec![
|
||||
Arc::new(StringArray::from(turns.iter().map(|t| t.source_file.as_str()).collect::<Vec<_>>())),
|
||||
Arc::new(StringArray::from(turns.iter().map(|t| t.conversation_title.as_str()).collect::<Vec<_>>())),
|
||||
Arc::new(Int32Array::from(turns.iter().map(|t| t.turn_number).collect::<Vec<_>>())),
|
||||
Arc::new(StringArray::from(turns.iter().map(|t| t.role.as_str()).collect::<Vec<_>>())),
|
||||
Arc::new(StringArray::from(turns.iter().map(|t| t.content.as_str()).collect::<Vec<_>>())),
|
||||
Arc::new(Int32Array::from(turns.iter().map(|t| t.page_number).collect::<Vec<_>>())),
|
||||
];
|
||||
|
||||
let batch = RecordBatch::try_new(schema.clone(), arrays)
|
||||
.map_err(|e| format!("RecordBatch error: {e}"))?;
|
||||
|
||||
Ok((schema, vec![batch]))
|
||||
}
|
||||
@@ -1,3 +1,4 @@
|
||||
pub mod chatgpt_parser;
|
||||
pub mod db_ingest;
|
||||
pub mod detect;
|
||||
pub mod csv_ingest;
|
||||
|
||||
@@ -43,7 +43,13 @@ pub async fn ingest_file(
|
||||
let (schema, batches) = match file_type {
|
||||
FileType::Csv => csv_ingest::parse_csv(content)?,
|
||||
FileType::Json | FileType::NdJson => json_ingest::parse_json(content)?,
|
||||
FileType::Pdf => pdf_ingest::parse_pdf(content, filename)?,
|
||||
FileType::Pdf => {
|
||||
// Try ChatGPT parser first, fall back to generic PDF
|
||||
match crate::chatgpt_parser::parse_chatgpt_pdf(content, filename) {
|
||||
Ok(result) => result,
|
||||
Err(_) => pdf_ingest::parse_pdf(content, filename)?,
|
||||
}
|
||||
}
|
||||
FileType::Text => parse_text_file(content, filename)?,
|
||||
FileType::Unknown => return Err(format!("unknown file type for '{filename}'")),
|
||||
};
|
||||
|
||||
@@ -40,48 +40,45 @@ pub fn chunk_text(
|
||||
}];
|
||||
}
|
||||
|
||||
// Work with char indices, not byte indices, to handle Unicode safely
|
||||
let chars: Vec<char> = text.chars().collect();
|
||||
let char_len = chars.len();
|
||||
let mut chunks = Vec::new();
|
||||
let mut start = 0;
|
||||
let mut start = 0usize; // char index
|
||||
let mut idx = 0u32;
|
||||
|
||||
while start < text.len() {
|
||||
let end = (start + chunk_size).min(text.len());
|
||||
while start < char_len {
|
||||
let end = (start + chunk_size).min(char_len);
|
||||
let chunk_str: String = chars[start..end].iter().collect();
|
||||
|
||||
// Try to break at a sentence or paragraph boundary
|
||||
let chunk_text = &text[start..end];
|
||||
let actual_end = if end < text.len() {
|
||||
// Look for last sentence boundary in the chunk
|
||||
if let Some(pos) = chunk_text.rfind(". ") {
|
||||
start + pos + 2
|
||||
} else if let Some(pos) = chunk_text.rfind('\n') {
|
||||
start + pos + 1
|
||||
let actual_end = if end < char_len {
|
||||
// Look for last sentence boundary
|
||||
if let Some(pos) = chunk_str.rfind(". ") {
|
||||
start + chunk_str[..pos + 2].chars().count()
|
||||
} else if let Some(pos) = chunk_str.rfind('\n') {
|
||||
start + chunk_str[..pos + 1].chars().count()
|
||||
} else if let Some(pos) = chunk_str.rfind(' ') {
|
||||
start + chunk_str[..pos + 1].chars().count()
|
||||
} else {
|
||||
// Fall back to word boundary
|
||||
if let Some(pos) = chunk_text.rfind(' ') {
|
||||
start + pos + 1
|
||||
} else {
|
||||
end
|
||||
}
|
||||
end
|
||||
}
|
||||
} else {
|
||||
end
|
||||
};
|
||||
|
||||
let chunk = text[start..actual_end].trim();
|
||||
if !chunk.is_empty() {
|
||||
let chunk: String = chars[start..actual_end].iter().collect();
|
||||
let trimmed = chunk.trim();
|
||||
if !trimmed.is_empty() {
|
||||
chunks.push(TextChunk {
|
||||
source: source.to_string(),
|
||||
doc_id: doc_id.to_string(),
|
||||
chunk_idx: idx,
|
||||
text: chunk.to_string(),
|
||||
text: trimmed.to_string(),
|
||||
});
|
||||
idx += 1;
|
||||
}
|
||||
|
||||
// Advance with overlap
|
||||
if actual_end >= text.len() {
|
||||
break;
|
||||
}
|
||||
if actual_end >= char_len { break; }
|
||||
start = if actual_end > overlap { actual_end - overlap } else { actual_end };
|
||||
}
|
||||
|
||||
|
||||
@@ -0,0 +1,72 @@
|
||||
{
|
||||
"id": "0ae8f29b-bf70-4c9d-8b64-6ea7cb7f76a1",
|
||||
"name": "rust_in_tech_stack",
|
||||
"schema_fingerprint": "314ee8ede68287240d7cec1403d252787c8ceb4ee2afa72cffcb0853ac3ac889",
|
||||
"objects": [
|
||||
{
|
||||
"bucket": "data",
|
||||
"key": "datasets/rust_in_tech_stack.parquet",
|
||||
"size_bytes": 99623,
|
||||
"created_at": "2026-03-28T03:32:50.769271381Z"
|
||||
}
|
||||
],
|
||||
"created_at": "2026-03-28T03:32:50.769272195Z",
|
||||
"updated_at": "2026-03-28T03:32:50.769710478Z",
|
||||
"description": "",
|
||||
"owner": "",
|
||||
"sensitivity": null,
|
||||
"columns": [
|
||||
{
|
||||
"name": "source_file",
|
||||
"data_type": "Utf8",
|
||||
"sensitivity": null,
|
||||
"description": "",
|
||||
"is_pii": false
|
||||
},
|
||||
{
|
||||
"name": "conversation_title",
|
||||
"data_type": "Utf8",
|
||||
"sensitivity": null,
|
||||
"description": "",
|
||||
"is_pii": false
|
||||
},
|
||||
{
|
||||
"name": "turn_number",
|
||||
"data_type": "Int32",
|
||||
"sensitivity": null,
|
||||
"description": "",
|
||||
"is_pii": false
|
||||
},
|
||||
{
|
||||
"name": "role",
|
||||
"data_type": "Utf8",
|
||||
"sensitivity": null,
|
||||
"description": "",
|
||||
"is_pii": false
|
||||
},
|
||||
{
|
||||
"name": "content",
|
||||
"data_type": "Utf8",
|
||||
"sensitivity": null,
|
||||
"description": "",
|
||||
"is_pii": false
|
||||
},
|
||||
{
|
||||
"name": "page_number",
|
||||
"data_type": "Int32",
|
||||
"sensitivity": null,
|
||||
"description": "",
|
||||
"is_pii": false
|
||||
}
|
||||
],
|
||||
"lineage": {
|
||||
"source_system": "pdf",
|
||||
"source_file": "Rust in Tech Stack.pdf",
|
||||
"ingest_job": "ingest-1774668770769",
|
||||
"ingest_timestamp": "2026-03-28T03:32:50.769271381Z",
|
||||
"parent_datasets": []
|
||||
},
|
||||
"freshness": null,
|
||||
"tags": [],
|
||||
"row_count": 49
|
||||
}
|
||||
@@ -0,0 +1,58 @@
|
||||
{
|
||||
"id": "24a05512-dc06-4884-9889-2ea978abdaec",
|
||||
"name": "processed_extractions",
|
||||
"schema_fingerprint": "c2853ba4c0cc5a897b37680fefea2708f51d51774226b00b72444e79d594c874",
|
||||
"objects": [
|
||||
{
|
||||
"bucket": "data",
|
||||
"key": "datasets/processed_extractions.parquet",
|
||||
"size_bytes": 28175,
|
||||
"created_at": "2026-03-28T05:16:57.437041979Z"
|
||||
}
|
||||
],
|
||||
"created_at": "2026-03-28T05:16:57.437042648Z",
|
||||
"updated_at": "2026-03-28T05:16:57.437403737Z",
|
||||
"description": "",
|
||||
"owner": "",
|
||||
"sensitivity": null,
|
||||
"columns": [
|
||||
{
|
||||
"name": "conversation",
|
||||
"data_type": "Utf8",
|
||||
"sensitivity": null,
|
||||
"description": "",
|
||||
"is_pii": false
|
||||
},
|
||||
{
|
||||
"name": "page",
|
||||
"data_type": "Int64",
|
||||
"sensitivity": null,
|
||||
"description": "",
|
||||
"is_pii": false
|
||||
},
|
||||
{
|
||||
"name": "type",
|
||||
"data_type": "Utf8",
|
||||
"sensitivity": null,
|
||||
"description": "",
|
||||
"is_pii": false
|
||||
},
|
||||
{
|
||||
"name": "content",
|
||||
"data_type": "Utf8",
|
||||
"sensitivity": null,
|
||||
"description": "",
|
||||
"is_pii": false
|
||||
}
|
||||
],
|
||||
"lineage": {
|
||||
"source_system": "csv",
|
||||
"source_file": "processed_extractions.csv",
|
||||
"ingest_job": "ingest-1774675017437",
|
||||
"ingest_timestamp": "2026-03-28T05:16:57.437041979Z",
|
||||
"parent_datasets": []
|
||||
},
|
||||
"freshness": null,
|
||||
"tags": [],
|
||||
"row_count": 543
|
||||
}
|
||||
@@ -0,0 +1,58 @@
|
||||
{
|
||||
"id": "3ead1a7a-810b-49d1-add8-752bab52dfae",
|
||||
"name": "processed_validated",
|
||||
"schema_fingerprint": "8078de214059a2a4582203b32c1172fcc311c2d3c762a3b075c07268308f1841",
|
||||
"objects": [
|
||||
{
|
||||
"bucket": "data",
|
||||
"key": "datasets/processed_validated.parquet",
|
||||
"size_bytes": 15271,
|
||||
"created_at": "2026-03-28T05:16:57.439472748Z"
|
||||
}
|
||||
],
|
||||
"created_at": "2026-03-28T05:16:57.439473552Z",
|
||||
"updated_at": "2026-03-28T05:16:57.439749766Z",
|
||||
"description": "",
|
||||
"owner": "",
|
||||
"sensitivity": null,
|
||||
"columns": [
|
||||
{
|
||||
"name": "conversation",
|
||||
"data_type": "Utf8",
|
||||
"sensitivity": null,
|
||||
"description": "",
|
||||
"is_pii": false
|
||||
},
|
||||
{
|
||||
"name": "page",
|
||||
"data_type": "Int64",
|
||||
"sensitivity": null,
|
||||
"description": "",
|
||||
"is_pii": false
|
||||
},
|
||||
{
|
||||
"name": "type",
|
||||
"data_type": "Utf8",
|
||||
"sensitivity": null,
|
||||
"description": "",
|
||||
"is_pii": false
|
||||
},
|
||||
{
|
||||
"name": "content",
|
||||
"data_type": "Utf8",
|
||||
"sensitivity": null,
|
||||
"description": "",
|
||||
"is_pii": false
|
||||
}
|
||||
],
|
||||
"lineage": {
|
||||
"source_system": "csv",
|
||||
"source_file": "processed_validated.csv",
|
||||
"ingest_job": "ingest-1774675017439",
|
||||
"ingest_timestamp": "2026-03-28T05:16:57.439472748Z",
|
||||
"parent_datasets": []
|
||||
},
|
||||
"freshness": null,
|
||||
"tags": [],
|
||||
"row_count": 60
|
||||
}
|
||||
@@ -0,0 +1,72 @@
|
||||
{
|
||||
"id": "54cc1ae9-14fe-40f6-88e4-7c623f57b577",
|
||||
"name": "crm_development_options",
|
||||
"schema_fingerprint": "bb04328fa99161403fa0993f59bf14adcf08eb551c7b0c5e9b9e305e5e0c8556",
|
||||
"objects": [
|
||||
{
|
||||
"bucket": "data",
|
||||
"key": "datasets/crm_development_options.parquet",
|
||||
"size_bytes": 50528,
|
||||
"created_at": "2026-03-28T03:30:29.036504470Z"
|
||||
}
|
||||
],
|
||||
"created_at": "2026-03-28T03:30:29.036504841Z",
|
||||
"updated_at": "2026-03-28T03:30:29.036642992Z",
|
||||
"description": "",
|
||||
"owner": "",
|
||||
"sensitivity": null,
|
||||
"columns": [
|
||||
{
|
||||
"name": "source_file",
|
||||
"data_type": "Utf8",
|
||||
"sensitivity": null,
|
||||
"description": "",
|
||||
"is_pii": false
|
||||
},
|
||||
{
|
||||
"name": "conversation_title",
|
||||
"data_type": "Utf8",
|
||||
"sensitivity": null,
|
||||
"description": "",
|
||||
"is_pii": false
|
||||
},
|
||||
{
|
||||
"name": "turn_number",
|
||||
"data_type": "Int32",
|
||||
"sensitivity": null,
|
||||
"description": "",
|
||||
"is_pii": false
|
||||
},
|
||||
{
|
||||
"name": "role",
|
||||
"data_type": "Utf8",
|
||||
"sensitivity": null,
|
||||
"description": "",
|
||||
"is_pii": false
|
||||
},
|
||||
{
|
||||
"name": "content",
|
||||
"data_type": "Utf8",
|
||||
"sensitivity": null,
|
||||
"description": "",
|
||||
"is_pii": false
|
||||
},
|
||||
{
|
||||
"name": "page_number",
|
||||
"data_type": "Int32",
|
||||
"sensitivity": null,
|
||||
"description": "",
|
||||
"is_pii": false
|
||||
}
|
||||
],
|
||||
"lineage": {
|
||||
"source_system": "pdf",
|
||||
"source_file": "CRM Development Options.pdf",
|
||||
"ingest_job": "ingest-1774668629036",
|
||||
"ingest_timestamp": "2026-03-28T03:30:29.036504470Z",
|
||||
"parent_datasets": []
|
||||
},
|
||||
"freshness": null,
|
||||
"tags": [],
|
||||
"row_count": 25
|
||||
}
|
||||
@@ -0,0 +1,72 @@
|
||||
{
|
||||
"id": "5d4bbdb1-fb53-4cea-86d5-309fe067111c",
|
||||
"name": "legacy_staffing_crm_search",
|
||||
"schema_fingerprint": "87b3fa58cae74468bf7fbbe32397b1a88bc281832bb0226b9eae5ce2501cda4e",
|
||||
"objects": [
|
||||
{
|
||||
"bucket": "data",
|
||||
"key": "datasets/legacy_staffing_crm_search.parquet",
|
||||
"size_bytes": 2722188,
|
||||
"created_at": "2026-03-28T03:34:14.444384147Z"
|
||||
}
|
||||
],
|
||||
"created_at": "2026-03-28T03:34:14.444384964Z",
|
||||
"updated_at": "2026-03-28T03:34:14.444631183Z",
|
||||
"description": "",
|
||||
"owner": "",
|
||||
"sensitivity": null,
|
||||
"columns": [
|
||||
{
|
||||
"name": "source_file",
|
||||
"data_type": "Utf8",
|
||||
"sensitivity": null,
|
||||
"description": "",
|
||||
"is_pii": false
|
||||
},
|
||||
{
|
||||
"name": "conversation_title",
|
||||
"data_type": "Utf8",
|
||||
"sensitivity": null,
|
||||
"description": "",
|
||||
"is_pii": false
|
||||
},
|
||||
{
|
||||
"name": "turn_number",
|
||||
"data_type": "Int32",
|
||||
"sensitivity": null,
|
||||
"description": "",
|
||||
"is_pii": false
|
||||
},
|
||||
{
|
||||
"name": "role",
|
||||
"data_type": "Utf8",
|
||||
"sensitivity": null,
|
||||
"description": "",
|
||||
"is_pii": false
|
||||
},
|
||||
{
|
||||
"name": "content",
|
||||
"data_type": "Utf8",
|
||||
"sensitivity": null,
|
||||
"description": "",
|
||||
"is_pii": false
|
||||
},
|
||||
{
|
||||
"name": "page_number",
|
||||
"data_type": "Int32",
|
||||
"sensitivity": null,
|
||||
"description": "",
|
||||
"is_pii": false
|
||||
}
|
||||
],
|
||||
"lineage": {
|
||||
"source_system": "pdf",
|
||||
"source_file": "Legacy Staffing CRM Search.pdf",
|
||||
"ingest_job": "ingest-1774668854444",
|
||||
"ingest_timestamp": "2026-03-28T03:34:14.444384147Z",
|
||||
"parent_datasets": []
|
||||
},
|
||||
"freshness": null,
|
||||
"tags": [],
|
||||
"row_count": 1
|
||||
}
|
||||
@@ -0,0 +1,51 @@
|
||||
{
|
||||
"id": "667478c3-47f9-4076-bab2-8293999d4db2",
|
||||
"name": "knowledge_insights",
|
||||
"schema_fingerprint": "36896987c7df0e78f5282208e3b8bbbaf9c017588d0bb42a20a7dbe97d8d4fa0",
|
||||
"objects": [
|
||||
{
|
||||
"bucket": "data",
|
||||
"key": "datasets/knowledge_insights.parquet",
|
||||
"size_bytes": 13099,
|
||||
"created_at": "2026-03-28T04:06:05.361963023Z"
|
||||
}
|
||||
],
|
||||
"created_at": "2026-03-28T04:06:05.361963273Z",
|
||||
"updated_at": "2026-03-28T04:06:05.362127023Z",
|
||||
"description": "",
|
||||
"owner": "",
|
||||
"sensitivity": null,
|
||||
"columns": [
|
||||
{
|
||||
"name": "conversation",
|
||||
"data_type": "Utf8",
|
||||
"sensitivity": null,
|
||||
"description": "",
|
||||
"is_pii": false
|
||||
},
|
||||
{
|
||||
"name": "page",
|
||||
"data_type": "Int64",
|
||||
"sensitivity": null,
|
||||
"description": "",
|
||||
"is_pii": false
|
||||
},
|
||||
{
|
||||
"name": "insight",
|
||||
"data_type": "Utf8",
|
||||
"sensitivity": null,
|
||||
"description": "",
|
||||
"is_pii": false
|
||||
}
|
||||
],
|
||||
"lineage": {
|
||||
"source_system": "csv",
|
||||
"source_file": "knowledge_insights.csv",
|
||||
"ingest_job": "ingest-1774670765361",
|
||||
"ingest_timestamp": "2026-03-28T04:06:05.361963023Z",
|
||||
"parent_datasets": []
|
||||
},
|
||||
"freshness": null,
|
||||
"tags": [],
|
||||
"row_count": 100
|
||||
}
|
||||
@@ -0,0 +1,51 @@
|
||||
{
|
||||
"id": "799c825c-950e-480e-a38e-4407377e43e3",
|
||||
"name": "knowledge_topics",
|
||||
"schema_fingerprint": "356c37c8fef56ce7f7882c80f1301cc2f70bcbf874719cb0628b42179874494c",
|
||||
"objects": [
|
||||
{
|
||||
"bucket": "data",
|
||||
"key": "datasets/knowledge_topics.parquet",
|
||||
"size_bytes": 14252,
|
||||
"created_at": "2026-03-28T04:06:05.358571822Z"
|
||||
}
|
||||
],
|
||||
"created_at": "2026-03-28T04:06:05.358572461Z",
|
||||
"updated_at": "2026-03-28T04:06:05.358833280Z",
|
||||
"description": "",
|
||||
"owner": "",
|
||||
"sensitivity": null,
|
||||
"columns": [
|
||||
{
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|
||||
"ingest_timestamp": "2026-03-28T04:06:05.360578855Z",
|
||||
"parent_datasets": []
|
||||
},
|
||||
"freshness": null,
|
||||
"tags": [],
|
||||
"row_count": 300
|
||||
}
|
||||
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@@ -0,0 +1,15 @@
|
||||
{
|
||||
"index_name": "observer_kb",
|
||||
"source": "chatgpt_pdfs",
|
||||
"model_name": "nomic-embed-text",
|
||||
"model_version": "latest",
|
||||
"dimensions": 768,
|
||||
"chunk_count": 477,
|
||||
"doc_count": 477,
|
||||
"chunk_size": 500,
|
||||
"overlap": 50,
|
||||
"storage_key": "vectors/observer_kb.parquet",
|
||||
"created_at": "2026-03-28T03:52:23.689193948Z",
|
||||
"build_time_secs": 11.022282,
|
||||
"chunks_per_sec": 43.275978
|
||||
}
|
||||
@@ -0,0 +1,15 @@
|
||||
{
|
||||
"index_name": "observer_kb_test",
|
||||
"source": "chatgpt",
|
||||
"model_name": "nomic-embed-text",
|
||||
"model_version": "latest",
|
||||
"dimensions": 768,
|
||||
"chunk_count": 100,
|
||||
"doc_count": 100,
|
||||
"chunk_size": 500,
|
||||
"overlap": 50,
|
||||
"storage_key": "vectors/observer_kb_test.parquet",
|
||||
"created_at": "2026-03-28T03:52:02.861946987Z",
|
||||
"build_time_secs": 1.8791063,
|
||||
"chunks_per_sec": 53.21679
|
||||
}
|
||||
@@ -0,0 +1,15 @@
|
||||
{
|
||||
"index_name": "observer_test",
|
||||
"source": "test",
|
||||
"model_name": "nomic-embed-text",
|
||||
"model_version": "latest",
|
||||
"dimensions": 768,
|
||||
"chunk_count": 15,
|
||||
"doc_count": 5,
|
||||
"chunk_size": 500,
|
||||
"overlap": 50,
|
||||
"storage_key": "vectors/observer_test.parquet",
|
||||
"created_at": "2026-03-28T03:46:22.891189902Z",
|
||||
"build_time_secs": 2.113568,
|
||||
"chunks_per_sec": 7.0970035
|
||||
}
|
||||
@@ -0,0 +1,15 @@
|
||||
{
|
||||
"index_name": "quick_test",
|
||||
"source": "test",
|
||||
"model_name": "nomic-embed-text",
|
||||
"model_version": "latest",
|
||||
"dimensions": 768,
|
||||
"chunk_count": 5,
|
||||
"doc_count": 5,
|
||||
"chunk_size": 500,
|
||||
"overlap": 50,
|
||||
"storage_key": "vectors/quick_test.parquet",
|
||||
"created_at": "2026-03-28T03:51:49.689474954Z",
|
||||
"build_time_secs": 1.3572433,
|
||||
"chunks_per_sec": 3.6839378
|
||||
}
|
||||
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@@ -0,0 +1,173 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Multi-pass deep analysis workflow.
|
||||
Demonstrates the architecture's ability to dynamically discover and organize
|
||||
knowledge from large-scale conversation data using local LLM processing.
|
||||
|
||||
Pass 1: Corpus overview — what's in the data
|
||||
Pass 2: Pattern detection — recurring themes across conversations
|
||||
Pass 3: Evolution tracking — how thinking changed over time
|
||||
Pass 4: Gap analysis — what's missing or unresolved
|
||||
Pass 5: Synthesis — actionable knowledge report
|
||||
"""
|
||||
|
||||
import json, urllib.request, time
|
||||
|
||||
API = "http://localhost:3100"
|
||||
|
||||
def sql(query):
|
||||
req = urllib.request.Request(f"{API}/query/sql",
|
||||
data=json.dumps({"sql": query}).encode(),
|
||||
headers={"Content-Type": "application/json"}, method="POST")
|
||||
return json.loads(urllib.request.urlopen(req, timeout=30).read())
|
||||
|
||||
def ask(prompt, max_tokens=800):
|
||||
req = urllib.request.Request(f"{API}/ai/generate",
|
||||
data=json.dumps({"prompt": prompt, "max_tokens": max_tokens, "temperature": 0.2}).encode(),
|
||||
headers={"Content-Type": "application/json"}, method="POST")
|
||||
return json.loads(urllib.request.urlopen(req, timeout=180).read())['text'].strip()
|
||||
|
||||
def search(query, top_k=10):
|
||||
req = urllib.request.Request(f"{API}/vectors/hnsw/search",
|
||||
data=json.dumps({"index_name": "observer_kb", "query": query, "top_k": top_k}).encode(),
|
||||
headers={"Content-Type": "application/json"}, method="POST")
|
||||
return json.loads(urllib.request.urlopen(req, timeout=30).read())['results']
|
||||
|
||||
print("=" * 70)
|
||||
print("DEEP ANALYSIS: Multi-Pass Knowledge Discovery")
|
||||
print("=" * 70)
|
||||
|
||||
# ============================================================
|
||||
print("\n" + "=" * 70)
|
||||
print("PASS 1: Corpus Overview")
|
||||
print("=" * 70)
|
||||
|
||||
# Get all conversation stats
|
||||
convs = sql("SELECT conversation, COUNT(*) as mentions FROM knowledge_topics GROUP BY conversation ORDER BY mentions DESC")
|
||||
topic_stats = sql("SELECT topic, COUNT(*) as n FROM knowledge_topics GROUP BY topic ORDER BY n DESC LIMIT 20")
|
||||
decision_count = sql("SELECT COUNT(*) as n FROM knowledge_decisions")['rows'][0]['n']
|
||||
insight_count = sql("SELECT COUNT(*) as n FROM knowledge_insights")['rows'][0]['n']
|
||||
question_count = sql("SELECT COUNT(*) as n FROM knowledge_questions")['rows'][0]['n']
|
||||
|
||||
print(f"\nCorpus: {len(convs['rows'])} conversations")
|
||||
print(f"Extracted: {decision_count} decisions, {insight_count} insights, {question_count} open questions")
|
||||
print(f"\nTop topics:")
|
||||
for r in topic_stats['rows'][:10]:
|
||||
print(f" {r['n']:>3}x {r['topic']}")
|
||||
|
||||
# Feed corpus overview to LLM for characterization
|
||||
overview_text = "\n".join([f" {r['topic']} ({r['n']}x)" for r in topic_stats['rows']])
|
||||
corpus_analysis = ask(f"""You are analyzing a knowledge base extracted from technical AI conversations.
|
||||
|
||||
Here are the most frequent topics discussed:
|
||||
{overview_text}
|
||||
|
||||
The corpus contains {decision_count} decisions, {insight_count} insights, and {question_count} open questions across {len(convs['rows'])} conversations.
|
||||
|
||||
Provide a 3-sentence characterization of what this person is building and what domains they're focused on. Be specific about the technical direction.""")
|
||||
|
||||
print(f"\nAI Characterization:")
|
||||
print(f" {corpus_analysis}")
|
||||
|
||||
# ============================================================
|
||||
print("\n" + "=" * 70)
|
||||
print("PASS 2: Cross-Conversation Pattern Detection")
|
||||
print("=" * 70)
|
||||
|
||||
# Find topics that appear across MULTIPLE conversations
|
||||
cross_topics = sql("SELECT topic, COUNT(DISTINCT conversation) as convs, COUNT(*) as total FROM knowledge_topics GROUP BY topic HAVING COUNT(DISTINCT conversation) > 1 ORDER BY convs DESC LIMIT 15")
|
||||
|
||||
print("\nTopics spanning multiple conversations (recurring themes):")
|
||||
for r in cross_topics['rows']:
|
||||
print(f" {r['topic']}: {r['convs']} conversations, {r['total']} mentions")
|
||||
|
||||
# Use semantic search to find related content across conversations
|
||||
print("\nSemantic pattern discovery:")
|
||||
patterns_to_check = [
|
||||
"architecture decisions about data storage and object storage",
|
||||
"trade-offs between different technologies and frameworks",
|
||||
"problems that were identified but not solved",
|
||||
"approaches that were tried and abandoned",
|
||||
]
|
||||
|
||||
for pattern in patterns_to_check:
|
||||
results = search(pattern, 3)
|
||||
if results:
|
||||
sources = set(r['doc_id'].split('_p')[0] for r in results)
|
||||
print(f"\n Pattern: \"{pattern}\"")
|
||||
print(f" Found across: {', '.join(sources)}")
|
||||
print(f" Top match [{results[0]['score']:.3f}]: {results[0]['chunk_text'][:100]}...")
|
||||
|
||||
# ============================================================
|
||||
print("\n" + "=" * 70)
|
||||
print("PASS 3: Decision Evolution")
|
||||
print("=" * 70)
|
||||
|
||||
# Get all decisions and analyze for contradictions/evolution
|
||||
decisions_sample = sql("SELECT conversation, page, decision FROM knowledge_decisions LIMIT 30")
|
||||
decisions_text = "\n".join([f"[{r['conversation']} p{r['page']}] {r['decision']}" for r in decisions_sample['rows']])
|
||||
|
||||
evolution = ask(f"""Analyze these technical decisions from multiple conversations over time.
|
||||
Identify:
|
||||
1. Decisions that CONTRADICT each other (thinking changed)
|
||||
2. Decisions that BUILD on each other (progressive refinement)
|
||||
3. Decisions that remain CONSISTENT (core principles)
|
||||
|
||||
Decisions:
|
||||
{decisions_text}
|
||||
|
||||
Format your response as three sections: CONTRADICTIONS, PROGRESSIVE, CONSISTENT. Be specific about which decisions you're comparing.""")
|
||||
|
||||
print(evolution)
|
||||
|
||||
# ============================================================
|
||||
print("\n" + "=" * 70)
|
||||
print("PASS 4: Gap Analysis")
|
||||
print("=" * 70)
|
||||
|
||||
questions_sample = sql("SELECT conversation, question FROM knowledge_questions LIMIT 20")
|
||||
questions_text = "\n".join([f"[{r['conversation']}] {r['question']}" for r in questions_sample['rows']])
|
||||
|
||||
insights_sample = sql("SELECT conversation, insight FROM knowledge_insights LIMIT 20")
|
||||
insights_text = "\n".join([f"[{r['conversation']}] {r['insight']}" for r in insights_sample['rows']])
|
||||
|
||||
gaps = ask(f"""You are analyzing a technical knowledge base.
|
||||
|
||||
OPEN QUESTIONS (things that were asked but not resolved):
|
||||
{questions_text}
|
||||
|
||||
KEY INSIGHTS (things that were learned):
|
||||
{insights_text}
|
||||
|
||||
Identify:
|
||||
1. Which open questions have actually been ANSWERED by insights in other conversations? (recovered knowledge)
|
||||
2. Which questions remain genuinely UNRESOLVED? (real gaps)
|
||||
3. What important topics are MISSING entirely? (blind spots)
|
||||
|
||||
Be specific.""")
|
||||
|
||||
print(gaps)
|
||||
|
||||
# ============================================================
|
||||
print("\n" + "=" * 70)
|
||||
print("PASS 5: Synthesis — Actionable Knowledge Report")
|
||||
print("=" * 70)
|
||||
|
||||
synthesis = ask(f"""Based on a deep analysis of {len(convs['rows'])} technical conversations containing {decision_count} decisions and {insight_count} insights, synthesize the following:
|
||||
|
||||
The person is building: {corpus_analysis[:200]}
|
||||
|
||||
Top recurring topics: {', '.join([r['topic'] for r in cross_topics['rows'][:8]])}
|
||||
|
||||
Key question: Given all this work, what are the 3 most important things this person should focus on NEXT? What patterns suggest they keep revisiting the same problems? What knowledge has been recovered that should be applied immediately?
|
||||
|
||||
Be direct and actionable. No filler.""")
|
||||
|
||||
print(synthesis)
|
||||
|
||||
print("\n" + "=" * 70)
|
||||
print("ANALYSIS COMPLETE")
|
||||
print(f"Processed: {len(convs['rows'])} conversations, {decision_count} decisions, {insight_count} insights")
|
||||
print(f"Infrastructure: SQL (DataFusion) + Vector Search (HNSW) + Local LLM (qwen2.5)")
|
||||
print(f"All local. No data left the machine.")
|
||||
print("=" * 70)
|
||||
@@ -0,0 +1,135 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Knowledge extraction pipeline: process all conversations through LLM,
|
||||
extract structured findings, build knowledge graph as Parquet tables.
|
||||
"""
|
||||
|
||||
import json, urllib.request, time, re
|
||||
|
||||
API = "http://localhost:3100"
|
||||
|
||||
def sql(query):
|
||||
req = urllib.request.Request(f"{API}/query/sql",
|
||||
data=json.dumps({"sql": query}).encode(),
|
||||
headers={"Content-Type": "application/json"}, method="POST")
|
||||
return json.loads(urllib.request.urlopen(req, timeout=30).read())
|
||||
|
||||
def generate(prompt, max_tokens=512):
|
||||
req = urllib.request.Request(f"{API}/ai/generate",
|
||||
data=json.dumps({"prompt": prompt, "max_tokens": max_tokens, "temperature": 0.1}).encode(),
|
||||
headers={"Content-Type": "application/json"}, method="POST")
|
||||
return json.loads(urllib.request.urlopen(req, timeout=120).read())
|
||||
|
||||
def ingest_csv(name, csv_text):
|
||||
boundary = "----kb" + str(int(time.time()))
|
||||
body = f"--{boundary}\r\nContent-Disposition: form-data; name=\"file\"; filename=\"{name}.csv\"\r\nContent-Type: text/csv\r\n\r\n{csv_text}\r\n--{boundary}--\r\n"
|
||||
req = urllib.request.Request(f"{API}/ingest/file?name={name}",
|
||||
data=body.encode(), headers={"Content-Type": f"multipart/form-data; boundary={boundary}"}, method="POST")
|
||||
try: return urllib.request.urlopen(req, timeout=15).read().decode()
|
||||
except: return "error"
|
||||
|
||||
# Get all conversation datasets
|
||||
old = ['call_log','placements','clients','threat_intel','timesheets','meta_runs','candidates',
|
||||
'job_orders','lab_trials','users','email_log','new_candidates','schema_test_v1',
|
||||
'schema_test_v2','stress_test_ingest','stress_ingest','knowledge_topics',
|
||||
'knowledge_decisions','knowledge_insights']
|
||||
ds = json.loads(urllib.request.urlopen(f"{API}/catalog/datasets").read())
|
||||
convs = [d for d in ds if d['name'] not in old]
|
||||
|
||||
print(f"Processing {len(convs)} conversations...")
|
||||
|
||||
# Knowledge tables
|
||||
all_topics = [] # (conversation, topic)
|
||||
all_decisions = [] # (conversation, decision, context)
|
||||
all_insights = [] # (conversation, page, insight, reasoning)
|
||||
all_questions = [] # (conversation, question)
|
||||
|
||||
for conv in convs:
|
||||
name = conv['name']
|
||||
pages = conv.get('row_count', 0) or 0
|
||||
if pages == 0: continue
|
||||
|
||||
print(f"\n{'='*50}")
|
||||
print(f"Extracting from: {name} ({pages} pages)")
|
||||
|
||||
# Sample pages (every 5th page for large convos, all for small)
|
||||
step = max(1, pages // 20) # ~20 samples max per conversation
|
||||
r = sql(f"SELECT turn_number, content FROM {name} WHERE turn_number % {step} = 1 ORDER BY turn_number LIMIT 20")
|
||||
|
||||
for row in r['rows']:
|
||||
page_num = row.get('turn_number', 0)
|
||||
content = (row.get('content', '') or '')[:2000]
|
||||
if len(content) < 100: continue
|
||||
|
||||
prompt = f"""Analyze this excerpt from a technical AI conversation. Extract structured knowledge.
|
||||
|
||||
TEXT:
|
||||
{content}
|
||||
|
||||
Output a JSON object with:
|
||||
- topics: list of technology/concept topics (max 5)
|
||||
- decisions: list of decisions made or recommended (max 3)
|
||||
- key_insight: single most important takeaway (1 sentence)
|
||||
- open_questions: unresolved questions (max 2)
|
||||
|
||||
Output ONLY valid JSON."""
|
||||
|
||||
try:
|
||||
resp = generate(prompt, 300)
|
||||
text = resp['text'].strip()
|
||||
# Extract JSON from response
|
||||
if '```' in text:
|
||||
text = text.split('```')[1].replace('json','',1).strip()
|
||||
# Find JSON object
|
||||
start = text.find('{')
|
||||
end = text.rfind('}') + 1
|
||||
if start >= 0 and end > start:
|
||||
data = json.loads(text[start:end])
|
||||
|
||||
for topic in data.get('topics', [])[:5]:
|
||||
all_topics.append(f"{name},{page_num},{topic}")
|
||||
|
||||
for decision in data.get('decisions', [])[:3]:
|
||||
all_decisions.append(f"{name},{page_num},{decision}")
|
||||
|
||||
insight = data.get('key_insight', '')
|
||||
if insight:
|
||||
all_insights.append(f"{name},{page_num},{insight}")
|
||||
|
||||
for q in data.get('open_questions', [])[:2]:
|
||||
all_questions.append(f"{name},{page_num},{q}")
|
||||
|
||||
print(f" p{page_num}: {len(data.get('topics',[]))} topics, {len(data.get('decisions',[]))} decisions")
|
||||
else:
|
||||
print(f" p{page_num}: no valid JSON")
|
||||
except Exception as e:
|
||||
print(f" p{page_num}: error — {str(e)[:50]}")
|
||||
|
||||
# Build and ingest knowledge tables
|
||||
print(f"\n{'='*50}")
|
||||
print(f"Extracted: {len(all_topics)} topics, {len(all_decisions)} decisions, {len(all_insights)} insights, {len(all_questions)} questions")
|
||||
|
||||
if all_topics:
|
||||
csv = "conversation,page,topic\n" + "\n".join(all_topics)
|
||||
print(f"Ingesting knowledge_topics ({len(all_topics)} rows)...", end=" ")
|
||||
print(ingest_csv("knowledge_topics", csv))
|
||||
|
||||
if all_decisions:
|
||||
csv = "conversation,page,decision\n" + "\n".join(all_decisions)
|
||||
print(f"Ingesting knowledge_decisions ({len(all_decisions)} rows)...", end=" ")
|
||||
print(ingest_csv("knowledge_decisions", csv))
|
||||
|
||||
if all_insights:
|
||||
csv = "conversation,page,insight\n" + "\n".join(all_insights)
|
||||
print(f"Ingesting knowledge_insights ({len(all_insights)} rows)...", end=" ")
|
||||
print(ingest_csv("knowledge_insights", csv))
|
||||
|
||||
if all_questions:
|
||||
csv = "conversation,page,question\n" + "\n".join(all_questions)
|
||||
print(f"Ingesting knowledge_questions ({len(all_questions)} rows)...", end=" ")
|
||||
print(ingest_csv("knowledge_questions", csv))
|
||||
|
||||
print(f"\nDone. Query with:")
|
||||
print(f" SELECT topic, COUNT(*) FROM knowledge_topics GROUP BY topic ORDER BY COUNT(*) DESC")
|
||||
print(f" SELECT * FROM knowledge_decisions")
|
||||
print(f" SELECT * FROM knowledge_insights")
|
||||
@@ -0,0 +1,163 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Process uploaded conversations through devop.live LLM Team modes.
|
||||
Runs multi-pass pipeline: extract → validate → organize.
|
||||
"""
|
||||
|
||||
import json, urllib.request, time, sys
|
||||
|
||||
LAKEHOUSE = "http://localhost:3100"
|
||||
LLM_TEAM = "http://localhost:5000"
|
||||
|
||||
def sql(query):
|
||||
req = urllib.request.Request(f"{LAKEHOUSE}/query/sql",
|
||||
data=json.dumps({"sql": query}).encode(),
|
||||
headers={"Content-Type": "application/json"}, method="POST")
|
||||
return json.loads(urllib.request.urlopen(req, timeout=30).read())
|
||||
|
||||
def run_mode(mode, prompt, models=None):
|
||||
"""Run a devop.live LLM Team mode and collect all responses."""
|
||||
if models is None:
|
||||
models = ["qwen2.5:latest"]
|
||||
|
||||
config = {"mode": mode, "prompt": prompt, "models": models}
|
||||
req = urllib.request.Request(f"{LLM_TEAM}/api/run",
|
||||
data=json.dumps(config).encode(),
|
||||
headers={"Content-Type": "application/json"}, method="POST")
|
||||
|
||||
resp = urllib.request.urlopen(req, timeout=300)
|
||||
|
||||
# Parse SSE stream
|
||||
responses = []
|
||||
final = None
|
||||
for line in resp:
|
||||
line = line.decode().strip()
|
||||
if line.startswith("data: "):
|
||||
try:
|
||||
event = json.loads(line[6:])
|
||||
if event.get("type") == "response":
|
||||
responses.append({
|
||||
"model": event.get("model", ""),
|
||||
"role": event.get("role", ""),
|
||||
"text": event.get("text", ""),
|
||||
})
|
||||
if event.get("role") == "final":
|
||||
final = event.get("text", "")
|
||||
except json.JSONDecodeError:
|
||||
pass
|
||||
|
||||
return {"responses": responses, "final": final}
|
||||
|
||||
def ingest_csv(name, csv_text):
|
||||
boundary = "----proc" + str(int(time.time()))
|
||||
body = f"--{boundary}\r\nContent-Disposition: form-data; name=\"file\"; filename=\"{name}.csv\"\r\nContent-Type: text/csv\r\n\r\n{csv_text}\r\n--{boundary}--\r\n"
|
||||
req = urllib.request.Request(f"{LAKEHOUSE}/ingest/file?name={name}",
|
||||
data=body.encode(), headers={"Content-Type": f"multipart/form-data; boundary={boundary}"}, method="POST")
|
||||
try: return urllib.request.urlopen(req, timeout=15).read().decode()
|
||||
except: return "error"
|
||||
|
||||
# Get conversation datasets
|
||||
old = ['call_log','placements','clients','threat_intel','timesheets','meta_runs','candidates',
|
||||
'job_orders','lab_trials','users','email_log','new_candidates','schema_test_v1',
|
||||
'schema_test_v2','stress_test_ingest','stress_ingest','knowledge_topics',
|
||||
'knowledge_decisions','knowledge_insights','knowledge_questions',
|
||||
'processed_extractions','processed_qa','processed_validated']
|
||||
ds = json.loads(urllib.request.urlopen(f"{LAKEHOUSE}/catalog/datasets").read())
|
||||
convs = [d for d in ds if d['name'] not in old and d.get('row_count',0) and d['row_count'] > 0]
|
||||
|
||||
print(f"{'='*60}")
|
||||
print(f"KNOWLEDGE PROCESSING PIPELINE")
|
||||
print(f"Using devop.live LLM Team modes")
|
||||
print(f"{'='*60}")
|
||||
print(f"\n{len(convs)} conversations to process")
|
||||
|
||||
all_extractions = []
|
||||
all_qa = []
|
||||
all_validated = []
|
||||
|
||||
for conv in convs:
|
||||
name = conv['name']
|
||||
pages = conv.get('row_count', 0) or 0
|
||||
if pages == 0: continue
|
||||
|
||||
print(f"\n{'='*60}")
|
||||
print(f"Processing: {name} ({pages} pages)")
|
||||
print(f"{'='*60}")
|
||||
|
||||
# Sample pages (every Nth for large docs)
|
||||
step = max(1, pages // 10)
|
||||
r = sql(f"SELECT turn_number, content FROM {name} WHERE turn_number % {step} = 1 ORDER BY turn_number LIMIT 10")
|
||||
|
||||
for row in r['rows']:
|
||||
page_num = row.get('turn_number', 0)
|
||||
content = (row.get('content', '') or '')[:3000]
|
||||
if len(content) < 100: continue
|
||||
|
||||
print(f"\n --- Page {page_num} ---")
|
||||
|
||||
# PASS 1: Extract (uses devop.live extract mode)
|
||||
print(f" [EXTRACT] Running extraction...", end=" ", flush=True)
|
||||
try:
|
||||
result = run_mode("extract", content)
|
||||
final = result.get("final", "")
|
||||
|
||||
# Parse the extraction response
|
||||
for resp in result["responses"]:
|
||||
if resp["role"].startswith("extraction"):
|
||||
text = resp["text"]
|
||||
# Try to find JSON in the response
|
||||
if "{" in text:
|
||||
start = text.find("{")
|
||||
end = text.rfind("}") + 1
|
||||
if end > start:
|
||||
try:
|
||||
data = json.loads(text[start:end])
|
||||
facts = data.get("facts", [])
|
||||
entities = data.get("entities", [])
|
||||
relationships = data.get("relationships", [])
|
||||
|
||||
for fact in facts[:5]:
|
||||
claim = fact if isinstance(fact, str) else fact.get("claim", str(fact))
|
||||
all_extractions.append(f'"{name}",{page_num},"fact","{claim[:200].replace(chr(34), chr(39))}"')
|
||||
|
||||
for ent in entities[:5]:
|
||||
ent_name = ent if isinstance(ent, str) else ent.get("name", str(ent))
|
||||
all_extractions.append(f'"{name}",{page_num},"entity","{ent_name[:200].replace(chr(34), chr(39))}"')
|
||||
|
||||
print(f"{len(facts)} facts, {len(entities)} entities, {len(relationships)} relationships")
|
||||
except json.JSONDecodeError:
|
||||
print("no JSON found")
|
||||
else:
|
||||
print("no structured output")
|
||||
|
||||
# Check verifier response
|
||||
if resp["role"] == "verifier":
|
||||
verified_text = resp["text"][:500]
|
||||
if verified_text.strip():
|
||||
all_validated.append(f'"{name}",{page_num},"verification","{verified_text[:200].replace(chr(34), chr(39))}"')
|
||||
|
||||
except Exception as e:
|
||||
print(f"error: {str(e)[:60]}")
|
||||
|
||||
# Small delay between pages to not overwhelm
|
||||
time.sleep(0.5)
|
||||
|
||||
# Store results
|
||||
print(f"\n{'='*60}")
|
||||
print(f"RESULTS")
|
||||
print(f"{'='*60}")
|
||||
print(f"Extractions: {len(all_extractions)}")
|
||||
print(f"Validated: {len(all_validated)}")
|
||||
|
||||
if all_extractions:
|
||||
csv = "conversation,page,type,content\n" + "\n".join(all_extractions)
|
||||
print(f"\nIngesting processed_extractions...", end=" ")
|
||||
print(ingest_csv("processed_extractions", csv))
|
||||
|
||||
if all_validated:
|
||||
csv = "conversation,page,type,content\n" + "\n".join(all_validated)
|
||||
print(f"Ingesting processed_validated...", end=" ")
|
||||
print(ingest_csv("processed_validated", csv))
|
||||
|
||||
print(f"\nDone. LLM Team modes processed {len(convs)} conversations.")
|
||||
print(f"Query: SELECT * FROM processed_extractions")
|
||||
Reference in New Issue
Block a user